code: def bad_quantile_transform(train_targets, non_train_targets):
      transformer = QuantileTransformer(
          output_distribution="normal", n_quantiles=100
      )
      train_targets[train_targets.columns] = transformer.fit_transform(
          train_targets.values
      )
      non_train_targets[train_targets.columns] = transformer.transform(
          non_train_targets.values
      )
      return (
          train_targets,
          non_train_targets,
          "i am the wrong type for an inversion result",
      )
  
file_path: hyperparameter_hunter/tests/integration_tests/feature_engineering/test_support.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, GBRT
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  import pytest
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
label_desc: Use image node_id 3 for calling a global function with example usage: bad_quantile_transform(train_targets, non_train_targets) and returns: train_targets,  non_train_targets,  str